Evaluating tag-reliant harvest estimators in Chinook salmon mixed-stock fisheries using simulations

Management of mixed-stock Chinook salmon (Oncorhynchus tshawytscha) fisheries requires balancing fishery access and conservation of vulnerable stocks. Although accurate, timely estimates of stock-specific harvest are crucial in achieving competing objectives, limited numbers of stock assignments (e.g., tag recoveries) can diminish the utility of estimates. We used a flexible simulation approach, applied to both a theoretical and real-world fishery case study, to compare the performance of competing monitoring alternatives and estimators for harvest. We sought to improve accuracy for point estimates of harvest and harvest trajectories over time. Bayesian models provided similarly accurate point estimates to existing models at high levels of data aggregation, generally improved estimates of harvest trajectories at intermediate aggregation, and reliable estimates of uncertainty. Incorporation of time-lagged prior information inconsistently improved estimates of harvest trajectories. Among monitoring alternatives yielding equal increases (33%) in coded wire tag recoveries, increasing tagging rates resulted in the greatest decrease in estimate uncertainty for target stocks (375% to 45.3%). Variable performances of mixed-stock harvest estimators suggest their use should considered on a stock- and fishery-specific basis, potentially using a simulation-based approach.

Automated summary

Management of mixed-stock Chinook salmon fisheries requires balancing access and conservation. This study compared monitoring alternatives and harvest estimators using simulation approach. Results showed that Bayesian models provided accurate point estimates and improved harvest trajectory estimates. Increasing tagging rates significantly reduced uncertainty in target stock estimates. The performance of mixed-stock harvest estimators varied, suggesting their use should be considered on a stock- and fishery-specific basis.